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文档列表
002.How to from plain texts to their classification/009. Hashing trick in spam filtering.mp4
64.2 MB
009.Statistical Machine Translation/028. Introduction to Machine Translation.mp4
59.9 MB
008.Topic models/027. The zoo of topic models.mp4
53.8 MB
002.How to from plain texts to their classification/006. Text preprocessing.mp4
53.7 MB
003.Simple deep learning for text classification/010. Neural networks for words.mp4
53.1 MB
005.Sequence tagging with probabilistic models/015. Hidden Markov Models.mp4
51.8 MB
002.How to from plain texts to their classification/007. Feature extraction from text.mp4
50.6 MB
012.Natural Language Understanding (NLU)/038. Intent classifier and slot tagger (NLU).mp4
50.3 MB
007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.mp4
48.0 MB
013.Dialog Manager (DM)/041. State tracking in DM.mp4
47.1 MB
009.Statistical Machine Translation/030. Word Alignment Models.mp4
45.2 MB
006.Deep Learning for the same tasks/019. Whether you need to predict a next word or a label - LSTM is here to help!.mp4
45.0 MB
007.Word and sentence embeddings/024. Why words From character to sentence embeddings.mp4
44.8 MB
012.Natural Language Understanding (NLU)/037. Task-oriented dialog systems.mp4
44.3 MB
005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.mp4
43.7 MB
011.Summarization and simplification tasks/036. Get to the point! Summarization with pointer-generator networks.mp4
43.0 MB
007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.mp4
42.0 MB
010.Encoder-decoder-attention arhitecture/033. How to deal with a vocabulary.mp4
42.0 MB
007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).mp4
41.4 MB
005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.mp4
41.2 MB
010.Encoder-decoder-attention arhitecture/034. How to implement a conversational chat-bot.mp4
40.0 MB
011.Summarization and simplification tasks/035. Sequence to sequence learning one-size fits all.mp4
38.5 MB
002.How to from plain texts to their classification/008. Linear models for sentiment analysis.mp4
37.9 MB
001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.mp4
36.7 MB
004.Language modeling it's all about counting!/012. Count! N-gram language models.mp4
35.5 MB
006.Deep Learning for the same tasks/018. Neural Language Models.mp4
33.0 MB
010.Encoder-decoder-attention arhitecture/032. Attention mechanism.mp4
32.7 MB
001.Introduction to NLP and our course/003. Main approaches in NLP.mp4
31.5 MB
012.Natural Language Understanding (NLU)/040. Adding lexicon to NLU.mp4
29.7 MB
007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.mp4
29.6 MB
003.Simple deep learning for text classification/011. Neural networks for characters.mp4
29.3 MB
004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.mp4
28.6 MB
013.Dialog Manager (DM)/042. Policy optimisation in DM.mp4
28.4 MB
004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.mp4
28.1 MB
001.Introduction to NLP and our course/004. Brief overview of the next weeks.mp4
27.4 MB
008.Topic models/025. Topic modeling a way to navigate through text collections.mp4
27.2 MB
008.Topic models/026. How to train PLSA.mp4
24.7 MB
010.Encoder-decoder-attention arhitecture/031. Encoder-decoder architecture.mp4
23.5 MB
009.Statistical Machine Translation/029. Noisy channel said in English, received in French.mp4
22.7 MB
013.Dialog Manager (DM)/043. Final remarks.mp4
22.7 MB
001.Introduction to NLP and our course/002. Welcome video.mp4
21.0 MB
012.Natural Language Understanding (NLU)/039. Adding context to NLU.mp4
17.9 MB
001.Introduction to NLP and our course/001. About this course.mp4
13.2 MB
002.How to from plain texts to their classification/009. Hashing trick in spam filtering.srt
23.4 kB
002.How to from plain texts to their classification/006. Text preprocessing.srt
20.7 kB
003.Simple deep learning for text classification/010. Neural networks for words.srt
19.5 kB
009.Statistical Machine Translation/028. Introduction to Machine Translation.srt
19.3 kB
012.Natural Language Understanding (NLU)/038. Intent classifier and slot tagger (NLU).srt
18.9 kB
002.How to from plain texts to their classification/007. Feature extraction from text.srt
18.8 kB
013.Dialog Manager (DM)/041. State tracking in DM.srt
17.9 kB
012.Natural Language Understanding (NLU)/037. Task-oriented dialog systems.srt
17.6 kB
008.Topic models/027. The zoo of topic models.srt
17.3 kB
005.Sequence tagging with probabilistic models/015. Hidden Markov Models.srt
17.0 kB
009.Statistical Machine Translation/030. Word Alignment Models.srt
15.8 kB
007.Word and sentence embeddings/021. Explicit and implicit matrix factorization.srt
15.8 kB
011.Summarization and simplification tasks/036. Get to the point! Summarization with pointer-generator networks.srt
15.7 kB
006.Deep Learning for the same tasks/019. Whether you need to predict a next word or a label - LSTM is here to help!.srt
15.3 kB
007.Word and sentence embeddings/024. Why words From character to sentence embeddings.srt
15.0 kB
010.Encoder-decoder-attention arhitecture/033. How to deal with a vocabulary.srt
14.8 kB
005.Sequence tagging with probabilistic models/017. MEMMs, CRFs and other sequential models for Named Entity Recognition.srt
14.8 kB
010.Encoder-decoder-attention arhitecture/034. How to implement a conversational chat-bot.srt
14.5 kB
004.Language modeling it's all about counting!/012. Count! N-gram language models.srt
13.9 kB
011.Summarization and simplification tasks/035. Sequence to sequence learning one-size fits all.srt
13.7 kB
005.Sequence tagging with probabilistic models/016. Viterbi algorithm what are the most probable tags.srt
13.4 kB
007.Word and sentence embeddings/023. Word analogies without magic king man + woman != queen.srt
13.1 kB
001.Introduction to NLP and our course/005. [Optional] Linguistic knowledge in NLP.srt
13.0 kB
007.Word and sentence embeddings/022. Word2vec and doc2vec (and how to evaluate them).srt
13.0 kB
002.How to from plain texts to their classification/008. Linear models for sentiment analysis.srt
12.9 kB
010.Encoder-decoder-attention arhitecture/032. Attention mechanism.srt
12.4 kB
006.Deep Learning for the same tasks/018. Neural Language Models.srt
12.1 kB
007.Word and sentence embeddings/020. Distributional semantics bee and honey vs. bee an bumblebee.srt
11.3 kB
003.Simple deep learning for text classification/011. Neural networks for characters.srt
10.7 kB
004.Language modeling it's all about counting!/013. Perplexity is our model surprised with a real text.srt
10.6 kB
013.Dialog Manager (DM)/042. Policy optimisation in DM.srt
10.3 kB
012.Natural Language Understanding (NLU)/040. Adding lexicon to NLU.srt
10.3 kB
001.Introduction to NLP and our course/003. Main approaches in NLP.srt
9.8 kB
001.Introduction to NLP and our course/004. Brief overview of the next weeks.srt
9.7 kB
004.Language modeling it's all about counting!/014. Smoothing what if we see new n-grams.srt
9.5 kB
008.Topic models/025. Topic modeling a way to navigate through text collections.srt
9.1 kB
008.Topic models/026. How to train PLSA.srt
8.8 kB
010.Encoder-decoder-attention arhitecture/031. Encoder-decoder architecture.srt
8.3 kB
009.Statistical Machine Translation/029. Noisy channel said in English, received in French.srt
7.7 kB
013.Dialog Manager (DM)/043. Final remarks.srt
7.6 kB
001.Introduction to NLP and our course/002. Welcome video.srt
7.4 kB
012.Natural Language Understanding (NLU)/039. Adding context to NLU.srt
7.1 kB
001.Introduction to NLP and our course/001. About this course.srt
3.3 kB
[FCS Forum].url
133 Bytes
[FreeCourseSite.com].url
127 Bytes
[CourseClub.NET].url
123 Bytes
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